A Fuzzy Clustering Model for Fuzzy Data with Outliers

TL;DR

This paper introduces a fuzzy clustering model for fuzzy data with outliers, utilizing Wasserstein distance and a transformation to Euclidean distance, demonstrated through simulation experiments.

Contribution

It presents a novel fuzzy clustering approach for fuzzy data with outliers, incorporating Wasserstein distance and outlier detection to improve clustering robustness.

Findings

Effective outlier detection and reduction in fuzzy clustering

Transformation reduces fuzzy data clustering to crisp data clustering

Simulation results demonstrate improved clustering performance

Abstract

In this paper a fuzzy clustering model for fuzzy data with outliers is proposed. The model is based on Wasserstein distance between interval valued data which is generalized to fuzzy data. In addition, Keller's approach is used to identify outliers and reduce their influences. We have also defined a transformation to change our distance to the Euclidean distance. With the help of this approach, the problem of fuzzy clustering of fuzzy data is reduced to fuzzy clustering of crisp data. In order to show the performance of the proposed clustering algorithm, two simulation experiments are discussed.